------------------------------------------------------------------------------ name: log: C:\Users\LabProf01\Documents\Probit_Logit_Tobit.log log type: text opened on: 10 May 2023, 15:36:29 . reg inlf nwifeinc educ exper expersq age kidslt6 kidsge6 Source | SS df MS Number of obs = 753 -------------+---------------------------------- F(7, 745) = 38.22 Model | 48.8080578 7 6.97257969 Prob > F = 0.0000 Residual | 135.919698 745 .182442547 R-squared = 0.2642 -------------+---------------------------------- Adj R-squared = 0.2573 Total | 184.727756 752 .245648611 Root MSE = .42713 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -.0034052 .0014485 -2.35 0.019 -.0062488 -.0005616 educ | .0379953 .007376 5.15 0.000 .023515 .0524756 exper | .0394924 .0056727 6.96 0.000 .0283561 .0506287 expersq | -.0005963 .0001848 -3.23 0.001 -.0009591 -.0002335 age | -.0160908 .0024847 -6.48 0.000 -.0209686 -.011213 kidslt6 | -.2618105 .0335058 -7.81 0.000 -.3275875 -.1960335 kidsge6 | .0130122 .013196 0.99 0.324 -.0128935 .0389179 _cons | .5855192 .154178 3.80 0.000 .2828442 .8881943 ------------------------------------------------------------------------------ . ´predict inlf_hat ´predict is not a valid command name r(199); . predict inlf_hat (option xb assumed; fitted values) . count inlf_hat<0 varlist not allowed r(101); . count if inlf_hat<0 16 . count if inlf_hat>1 17 . probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -402.06651 Iteration 2: log likelihood = -401.30273 Iteration 3: log likelihood = -401.30219 Iteration 4: log likelihood = -401.30219 Probit regression Number of obs = 753 LR chi2(7) = 227.14 Prob > chi2 = 0.0000 Log likelihood = -401.30219 Pseudo R2 = 0.2206 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378 educ | .1309047 .0252542 5.18 0.000 .0814074 .180402 exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311 expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111 age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376 kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029 kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179 _cons | .2700768 .508593 0.53 0.595 -.7267473 1.266901 ------------------------------------------------------------------------------ . probit inlf Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -514.8732 Probit regression Number of obs = 753 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -514.8732 Pseudo R2 = 0.0000 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | .1722846 .0459205 3.75 0.000 .0822821 .2622871 ------------------------------------------------------------------------------ . display 1-(-401.30219 /-514.8732 ) .22058054 . display 2*( -401.30219 --514.8732 ) 227.14202 . probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -402.06651 Iteration 2: log likelihood = -401.30273 Iteration 3: log likelihood = -401.30219 Iteration 4: log likelihood = -401.30219 Probit regression Number of obs = 753 LR chi2(7) = 227.14 Prob > chi2 = 0.0000 Log likelihood = -401.30219 Pseudo R2 = 0.2206 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378 educ | .1309047 .0252542 5.18 0.000 .0814074 .180402 exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311 expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111 age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376 kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029 kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179 _cons | .2700768 .508593 0.53 0.595 -.7267473 1.266901 ------------------------------------------------------------------------------ . estimates store UR . probit inlf nwifeinc educ exper expersq age Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -432.95213 Iteration 2: log likelihood = -432.80877 Iteration 3: log likelihood = -432.80875 Probit regression Number of obs = 753 LR chi2(5) = 164.13 Prob > chi2 = 0.0000 Log likelihood = -432.80875 Pseudo R2 = 0.1594 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -.0112811 .0046216 -2.44 0.015 -.0203393 -.0022229 educ | .1051111 .0239911 4.38 0.000 .0580894 .1521327 exper | .1253479 .0181857 6.89 0.000 .0897046 .1609912 expersq | -.0020406 .0005906 -3.46 0.001 -.0031982 -.000883 age | -.0286742 .006845 -4.19 0.000 -.0420902 -.0152581 _cons | -.6190137 .4165484 -1.49 0.137 -1.435434 .1974062 ------------------------------------------------------------------------------ . estimates store R . lrtest UR R Likelihood-ratio test LR chi2(2) = 63.01 (Assumption: R nested in UR) Prob > chi2 = 0.0000 . probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 Iteration 0: log likelihood = -514.8732 Iteration 1: log likelihood = -402.06651 Iteration 2: log likelihood = -401.30273 Iteration 3: log likelihood = -401.30219 Iteration 4: log likelihood = -401.30219 Probit regression Number of obs = 753 LR chi2(7) = 227.14 Prob > chi2 = 0.0000 Log likelihood = -401.30219 Pseudo R2 = 0.2206 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378 educ | .1309047 .0252542 5.18 0.000 .0814074 .180402 exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311 expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111 age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376 kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029 kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179 _cons | .2700768 .508593 0.53 0.595 -.7267473 1.266901 ------------------------------------------------------------------------------ . margins, dydx(educ) Average marginal effects Number of obs = 753 Model VCE : OIM Expression : Pr(inlf), predict() dy/dx w.r.t. : educ ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0393703 .0072216 5.45 0.000 .0252161 .0535244 ------------------------------------------------------------------------------ . sum educ Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- educ | 753 12.28685 2.280246 5 17 . margins, dydx(educ) at(educ=(5(2)17)) Average marginal effects Number of obs = 753 Model VCE : OIM Expression : Pr(inlf), predict() dy/dx w.r.t. : educ 1._at : educ = 5 2._at : educ = 7 3._at : educ = 9 4._at : educ = 11 5._at : educ = 13 6._at : educ = 15 7._at : educ = 17 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | _at | 1 | .0356954 .0041186 8.67 0.000 .027623 .0437677 2 | .0391237 .0061814 6.33 0.000 .0270085 .0512389 3 | .0410348 .0075541 5.43 0.000 .026229 .0558406 4 | .0412382 .0079948 5.16 0.000 .0255686 .0569078 5 | .0397488 .0074669 5.32 0.000 .0251139 .0543838 6 | .0367742 .0061253 6.00 0.000 .0247688 .0487796 7 | .03267 .0042702 7.65 0.000 .0243005 .0410394 ------------------------------------------------------------------------------ . margins, dydx(educ) at(educ=(5(1)17)) Average marginal effects Number of obs = 753 Model VCE : OIM Expression : Pr(inlf), predict() dy/dx w.r.t. : educ 1._at : educ = 5 2._at : educ = 6 3._at : educ = 7 4._at : educ = 8 5._at : educ = 9 6._at : educ = 10 7._at : educ = 11 8._at : educ = 12 9._at : educ = 13 10._at : educ = 14 11._at : educ = 15 12._at : educ = 16 13._at : educ = 17 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | _at | 1 | .0356954 .0041186 8.67 0.000 .027623 .0437677 2 | .0375797 .0052132 7.21 0.000 .027362 .0477974 3 | .0391237 .0061814 6.33 0.000 .0270085 .0512389 4 | .0402855 .0069739 5.78 0.000 .026617 .0539541 5 | .0410348 .0075541 5.43 0.000 .026229 .0558406 6 | .0413539 .0078981 5.24 0.000 .0258739 .0568339 7 | .0412382 .0079948 5.16 0.000 .0255686 .0569078 8 | .0406963 .0078464 5.19 0.000 .0253177 .0560749 9 | .0397488 .0074669 5.32 0.000 .0251139 .0543838 10 | .0384277 .0068818 5.58 0.000 .0249396 .0519158 11 | .0367742 .0061253 6.00 0.000 .0247688 .0487796 12 | .0348369 .0052391 6.65 0.000 .0245684 .0451054 13 | .03267 .0042702 7.65 0.000 .0243005 .0410394 ------------------------------------------------------------------------------ . marginsplot, xdimension(at(educ)) Variables that uniquely identify margins: educ . reg hours nwifeinc educ exper expersq age kidslt6 kidsge6 Source | SS df MS Number of obs = 7 > 53 -------------+---------------------------------- F(7, 745) = 38. > 50 Model | 151647606 7 21663943.7 Prob > F = 0.00 > 00 Residual | 419262118 745 562767.944 R-squared = 0.26 > 56 -------------+---------------------------------- Adj R-squared = 0.25 > 87 Total | 570909724 752 759188.463 Root MSE = 750. > 18 ---------------------------------------------------------------------------- > -- hours | Coef. Std. Err. t P>|t| [95% Conf. Interva > l] -------------+-------------------------------------------------------------- > -- nwifeinc | -3.446636 2.544 -1.35 0.176 -8.440898 1.5476 > 26 educ | 28.76112 12.95459 2.22 0.027 3.329283 54.192 > 97 exper | 65.67251 9.962983 6.59 0.000 46.11365 85.231 > 38 expersq | -.7004939 .3245501 -2.16 0.031 -1.337635 -.06335 > 24 age | -30.51163 4.363868 -6.99 0.000 -39.07858 -21.944 > 69 kidslt6 | -442.0899 58.8466 -7.51 0.000 -557.6148 -326.5 > 65 kidsge6 | -32.77923 23.17622 -1.41 0.158 -78.2777 12.719 > 24 _cons | 1330.482 270.7846 4.91 0.000 798.8906 1862.0 > 74 ---------------------------------------------------------------------------- > -- . reg hours nwifeinc educ exper expersq age kidslt6 kidsge6 Source | SS df MS Number of obs = 753 -------------+---------------------------------- F(7, 745) = 38.50 Model | 151647606 7 21663943.7 Prob > F = 0.0000 Residual | 419262118 745 562767.944 R-squared = 0.2656 -------------+---------------------------------- Adj R-squared = 0.2587 Total | 570909724 752 759188.463 Root MSE = 750.18 ------------------------------------------------------------------------------ hours | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -3.446636 2.544 -1.35 0.176 -8.440898 1.547626 educ | 28.76112 12.95459 2.22 0.027 3.329283 54.19297 exper | 65.67251 9.962983 6.59 0.000 46.11365 85.23138 expersq | -.7004939 .3245501 -2.16 0.031 -1.337635 -.0633524 age | -30.51163 4.363868 -6.99 0.000 -39.07858 -21.94469 kidslt6 | -442.0899 58.8466 -7.51 0.000 -557.6148 -326.565 kidsge6 | -32.77923 23.17622 -1.41 0.158 -78.2777 12.71924 _cons | 1330.482 270.7846 4.91 0.000 798.8906 1862.074 ------------------------------------------------------------------------------ . predict inlf_hat variable inlf_hat already defined r(110); . . predict hours_hat (option xb assumed; fitted values) . count if hours_hat<0 39 . tobit hours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0) Refining starting values: Grid node 0: log likelihood = -3961.1577 Fitting full model: Iteration 0: log likelihood = -3961.1577 Iteration 1: log likelihood = -3836.8928 Iteration 2: log likelihood = -3819.2637 Iteration 3: log likelihood = -3819.0948 Iteration 4: log likelihood = -3819.0946 Tobit regression Number of obs = 753 Uncensored = 428 Limits: lower = 0 Left-censored = 325 upper = +inf Right-censored = 0 LR chi2(7) = 271.59 Prob > chi2 = 0.0000 Log likelihood = -3819.0946 Pseudo R2 = 0.0343 ------------------------------------------------------------------------------ hours | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -8.814226 4.459089 -1.98 0.048 -17.56808 -.0603706 educ | 80.64541 21.58318 3.74 0.000 38.27441 123.0164 exper | 131.564 17.27935 7.61 0.000 97.64211 165.486 expersq | -1.864153 .5376606 -3.47 0.001 -2.919661 -.8086455 age | -54.40491 7.418483 -7.33 0.000 -68.9685 -39.84133 kidslt6 | -894.0202 111.8777 -7.99 0.000 -1113.653 -674.3875 kidsge6 | -16.21805 38.6413 -0.42 0.675 -92.07668 59.64057 _cons | 965.3068 446.4351 2.16 0.031 88.88827 1841.725 -------------+---------------------------------------------------------------- var(e.hours)| 1258927 93304.48 1088458 1456093 ------------------------------------------------------------------------------ . sum hours Variable | Obs Mean Std. Dev. Min > Max -------------+----------------------------------------------------- > ---- hours | 753 740.5764 871.3142 0 > 4950 . sum hours if inlf==1 Variable | Obs Mean Std. Dev. Min > Max -------------+----------------------------------------------------- > ---- hours | 428 1302.93 776.2744 12 > 4950 . margins, dydx( ) required r(100); . margins, dydx(*) predict(ystar(0,.)) Average marginal effects Number of obs = > 753 Model VCE : OIM Expression : E(hours*|hours>0), predict(ystar(0,.)) dy/dx w.r.t. : nwifeinc educ exper expersq age kidslt6 kidsge6 ------------------------------------------------------------------- > ----------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf > . Interval] -------------+----------------------------------------------------- > ----------- nwifeinc | -5.188619 2.621409 -1.98 0.048 -10.32649 > -.0507514 educ | 47.47306 12.6214 3.76 0.000 22.73558 > 72.21054 exper | 77.44703 9.99765 7.75 0.000 57.85199 > 97.04206 expersq | -1.09736 .3155945 -3.48 0.001 -1.715914 > -.4788063 age | -32.02622 4.29211 -7.46 0.000 -40.4386 > -23.61384 kidslt6 | -526.2776 64.70619 -8.13 0.000 -653.0994 > -399.4558 kidsge6 | -9.546986 22.75224 -0.42 0.675 -54.14056 > 35.04659 ------------------------------------------------------------------- > ----------- . margins, dydx(*) predict(ystar(0,.)) Average marginal effects Number of obs = > 753 Model VCE : OIM Expression : E(hours*|hours>0), predict(ystar(0,.)) dy/dx w.r.t. : nwifeinc educ exper expersq age kidslt6 kidsge6 ------------------------------------------------------------------------ > ------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Int > erval] -------------+---------------------------------------------------------- > ------ nwifeinc | -5.188619 2.621409 -1.98 0.048 -10.32649 -.0 > 507514 educ | 47.47306 12.6214 3.76 0.000 22.73558 72 > .21054 exper | 77.44703 9.99765 7.75 0.000 57.85199 97 > .04206 expersq | -1.09736 .3155945 -3.48 0.001 -1.715914 -.4 > 788063 age | -32.02622 4.29211 -7.46 0.000 -40.4386 -23 > .61384 kidslt6 | -526.2776 64.70619 -8.13 0.000 -653.0994 -39 > 9.4558 kidsge6 | -9.546986 22.75224 -0.42 0.675 -54.14056 35 > .04659 ------------------------------------------------------------------------ > ------ . margins, dydx(*) predict(ystar(0,.)) Average marginal effects Number of obs = 753 Model VCE : OIM Expression : E(hours*|hours>0), predict(ystar(0,.)) dy/dx w.r.t. : nwifeinc educ exper expersq age kidslt6 kidsge6 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | -5.188619 2.621409 -1.98 0.048 -10.32649 -.0507514 educ | 47.47306 12.6214 3.76 0.000 22.73558 72.21054 exper | 77.44703 9.99765 7.75 0.000 57.85199 97.04206 expersq | -1.09736 .3155945 -3.48 0.001 -1.715914 -.4788063 age | -32.02622 4.29211 -7.46 0.000 -40.4386 -23.61384 kidslt6 | -526.2776 64.70619 -8.13 0.000 -653.0994 -399.4558 kidsge6 | -9.546986 22.75224 -0.42 0.675 -54.14056 35.04659 ------------------------------------------------------------------------------ . margins, dydx(*) predict(ystar(0,.)) at(kidslt6=0(1)5) (1) invalid statistic r(198); . sum kidslt6 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- kidslt6 | 753 .2377158 .523959 0 3 . margins, dydx(*) predict(ystar(0,.)) at(kidslt6=0(1)3) (1) invalid statistic r(198); . margins, dydx(*) predict(ystar(0,.)) at(kidslt6=(0(1)3)) Average marginal effects Number of obs = 753 Model VCE : OIM Expression : E(hours*|hours>0), predict(ystar(0,.)) dy/dx w.r.t. : nwifeinc educ exper expersq age kidslt6 kidsge6 1._at : kidslt6 = 0 2._at : kidslt6 = 1 3._at : kidslt6 = 2 4._at : kidslt6 = 3 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nwifeinc | _at | 1 | -5.737974 2.896324 -1.98 0.048 -11.41466 -.061284 2 | -3.460937 1.763189 -1.96 0.050 -6.916723 -.0051506 3 | -1.516725 .8414605 -1.80 0.071 -3.165957 .1325072 4 | -.449882 .3197702 -1.41 0.159 -1.07662 .1768561 -------------+---------------------------------------------------------------- educ | _at | 1 | 52.49937 13.98256 3.75 0.000 25.09406 79.90468 2 | 31.6657 8.567111 3.70 0.000 14.87447 48.45693 3 | 13.87721 4.724939 2.94 0.003 4.616505 23.13792 4 | 4.116177 2.27074 1.81 0.070 -.334392 8.566747 -------------+---------------------------------------------------------------- exper | _at | 1 | 85.64689 10.90113 7.86 0.000 64.28107 107.0127 2 | 51.65908 7.962874 6.49 0.000 36.05214 67.26603 3 | 22.63914 6.283291 3.60 0.000 10.32411 34.95416 4 | 6.715087 3.565697 1.88 0.060 -.273551 13.70372 -------------+---------------------------------------------------------------- expersq | _at | 1 | -1.213545 .3470341 -3.50 0.000 -1.89372 -.5333712 2 | -.7319664 .2204775 -3.32 0.001 -1.164094 -.2998384 3 | -.3207778 .1228638 -2.61 0.009 -.5615863 -.0799693 4 | -.0951472 .0567727 -1.68 0.094 -.2064196 .0161252 -------------+---------------------------------------------------------------- age | _at | 1 | -35.41706 4.875047 -7.26 0.000 -44.97198 -25.86215 2 | -21.36228 2.845592 -7.51 0.000 -26.93954 -15.78502 3 | -9.361831 2.139335 -4.38 0.000 -13.55485 -5.168811 4 | -2.776851 1.319423 -2.10 0.035 -5.362873 -.1908286 -------------+---------------------------------------------------------------- kidslt6 | _at | 1 | -581.9983 77.75742 -7.48 0.000 -734.4001 -429.5966 2 | -351.0402 23.76648 -14.77 0.000 -397.6216 -304.4587 3 | -153.8403 17.50885 -8.79 0.000 -188.157 -119.5235 4 | -45.63119 17.2385 -2.65 0.008 -79.41803 -11.84435 -------------+---------------------------------------------------------------- kidsge6 | _at | 1 | -10.55779 25.16718 -0.42 0.675 -59.88455 38.76896 2 | -6.368076 15.13764 -0.42 0.674 -36.03731 23.30116 3 | -2.790753 6.619752 -0.42 0.673 -15.76523 10.18372 4 | -.8277767 1.977434 -0.42 0.676 -4.703476 3.047923 ------------------------------------------------------------------------------ . log close name: log: C:\Users\LabProf01\Documents\Probit_Logit_Tobit.log log type: text closed on: 10 May 2023, 16:25:46 -----------------------------------------------------------------------------------